#66DaysofData Member Spotlight: Thinam Tamang

This is the second in a weekly series spotlighting different members of the 66DaysofData discord community, an initiative started by data scientist and YouTuber Ken Jee. You can find out more about the initiative here and here. The intent of this series is to celebrate those who have made it through the challenge already (although it never truly ends!); share their stories; learn from their successes and failures; and hopefully inspire anybody else walking this path, wherever you happen to be in your journey. If you’re interested in joining the community, you can do so here.

Sometimes, it starts with a bit of serendipity and snowballs from there. Thinam Tamang’s impressive journey to date started with some words from a college professor. Thinam was in school to study computer science because he was good at math. It was there a professor told him with his combination of math and programming, data science might be a field worth considering.

Fast forwarding a year and a half, he’s now working as a research assistant in the Center of Data Mining and Biomedical Informatics lab with Chanin (also known online as Data Professor), with a couple data science internships under his belt.

However, it took a lot of work to get to where he is today. With his professor’s nudging, Thinam began to research the field and decided it was something he wanted to pursue. Unfortunately, he didn’t have many places to turn to for advice; the closest thing to data science he saw people doing was web development. This certainly didn’t stop him from finding resources on his own though, like the popular IBM data science specialization on Coursera, Datacamp with their career tracks and many others. It took him six months to complete the IBM specialization because almost everything was brand new to him. I admire his patience, his persistence and his humility. It would’ve been easy to give up and go back to what he’d already been doing, having invested very little into this new path at the time.

Eventually, he discovered Ken Jee’s YouTube channel and joined the #66DaysofData initiative… which took his learning to the next level. What stood out most to me in our conversation was how the initiative actually forced him to think beyond the end of the day. Where Thinam would push hard for a couple days, then need a break (rinse and repeat) before starting the challenge, setting an expectation of consistency no matter how little helped him look longer term.

It doesn’t matter how much you do in one day; what matters is how much you can do sustainably for the long run.

Thinam acknowledged this too. People should not make his mistake (and mine!) of being in a hurry. You may get demotivated that so many people are doing great (or so it appears), but consistency and continuity are super important.

If you have Thinam’s joy for learning though, you may also inspire the opposite in others by sharing your journey. He recommends sharing the things you learn publicly because it can start a virtuous cycle in your circle. You can help motivate others by sharing, which in turn may motivate you to keep learning and sharing once you see the positive effect you can have on others. He gives Ken a lot of credit for encouraging him to document publicly — and for giving him exposure in the community by reviewing his resume, GitHub and LinkedIn in a YouTube video.

These days, it seems Thinam has gotten over the first (and maybe hardest) hurdle in this long quest to master data science. Now, he spends 2–3 hours every day reading and working on projects after his school work is complete. Until recently, he was also working with professors at his school on a continuous speech recognition project using Kaldi, an open-source package for dealing with speech data. After working on Chanin’s bio-informatics paper, he’s back for more and currently working on a deep learning project. If I were to share one final piece of advice from our conversation:

You need to find the learning methods that work for you.

While he started with Coursera specializations, he now finds that he learns best by diving deep into books and implementing concepts and code into his own projects, then sharing the things he’s learned in his own words.

He hopes to find work in data science as a machine learning engineer after he graduates from college in a few years. With the foundation he’s already set and the work he’s putting in, I think the sky’s the limit for Thinam.

You can read more from Thinam Tamang on LinkedIn, on Twitter or follow his machine learning/deep learning and natural language processing journeys on GitHub.

Finally, if you’d like to join a community of data science learners in all stages of the journey, feel free to join the 66DaysofData discord server.